Iranian Institute of Industrial EngineeringJournal of Industrial and Systems Engineering1735-827212Special issue on Statistical Processes and Statistical Modelling20191101Functional process capability indices for nonlinear profile0076523ENAhmad Mohammad Pour LarimiDepartment of Industrial Engineering, Mazandaran Institute of Technology, Babol, IranRamezan Nemati keshteliFaculty of Engineering Eastern Guilan, Guilan University, IranAbdul Sattar SafaeiDepartment of Industrial Engineering, Babol University of Technology, P.O. Box 47148 - 71167, Babol, IranJournal Article20181030A profile is a relationship between a response variable and one or more independent variables being controlled during the time. Process Capability Indices (PCI) are measured to evaluate the performance of processes in producing conforming products. Despite frequent applications of profile and a variety of available methods to monitor its different types, little researches have been carried out on determining capability indices of profile process. PCIs such as and in profile state, are used to evaluate process capability in producing conforming profiles. This paper presents a functional approach for nonlinear profiles which usually expressed as nonlinear regression. Thus, functions such as pertaining technical specification limits, mean and natural tolerance limits are determined as nonlinear profiles and also functional limit is applied to determine Functional Capability Indices (FCI) and of functional nonlinear profile. Easy calculation and the ability to calculate FC in each period and at each point are from the advantages of this method over the other methods.
profile monitoring
non-linear profile
functional process capability index
vertical density profile
Iranian Institute of Industrial EngineeringJournal of Industrial and Systems Engineering1735-827212Special issue on Statistical Processes and Statistical Modelling20191101New phase II control chart for monitoring ordinal contingency table based processes0078697ENAhmad HakimiDepartment of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, IranHiwa FarughiDepartment of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran0000-0001-9745-9691Amirhossein AmiriDepartment of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.0000-0002-2385-8910Jamal ArkatDepartment of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, IranJournal Article20181121In some statistical process monitoring applications, quality of a process or product is described by more than one ordinal factors called ordinal multivariate process. To show the relationship between these factors, an ordinal contingency table is used and modeled with ordinal log-linear model. In this paper, a new control charts based on ordinal-normal statistic is developed to monitor the ordinal log-linear model based processes in Phase II. Performance of the proposed control chart is evaluated through simulation studies and a real numerical example. In addition, to show the efficiency of ordinal-normal control chart, performance of the proposed control chart is compared with an existing Generalized-<em>p </em>chart. Results show the better performance of the proposed control chart in detecting the out-of-control condition.
Statistical process monitoring
ordinal contingency table
ordinal-normal control chart
phase
Iranian Institute of Industrial EngineeringJournal of Industrial and Systems Engineering1735-827212Special issue on Statistical Processes and Statistical Modelling20191101An economic-statistical model for production and maintenance planning under adaptive non-central chi-square control chart0078698ENali salmasniaDepartment of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iranfarzaneh SoltanyDepartment of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iranmaryam norooziDepartment of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iranbehnam AbdzadehDepartment of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, IranJournal Article20181121Most of the inventory control models assume that quality defect never happens, which means production process is perfect. However, in real manufacturing processes, the production process starts its operation in the in-control state; but after a period of time, shifts to the out-of-control state because of occurrence of some disturbances. In this paper, in order to approach the model to real manufacturing conditions, a process is considered in which quality defect and machine deterioration may occur. Since the adaptive control charts detect the occurrence of assignable cause quicker than the traditional control charts, an adaptive non-central chi-square control chart is designed, which monitors the process mean and variance, simultaneously. In addition, to reduce the failure rate of the machine, two types of maintenance policies consisting of reactive and preventive are planned. Then, the particle swarm optimization algorithm is employed to minimize the overall cost per cycle involving inventory cost, quality loss cost, inspection cost and maintenance cost subject to statistical quality constraints. Finally, to demonstrate the effectiveness of the suggested approach, two comparative studies are presented. The first one confirms that integration of production planning, maintenance policy and statistical process monitoring leads to a significant increase in the cost savings. The second one indicates superiority of the developed adaptive control chart in comparison with the control chart with the fixed parameters.
Production Planning
maintenance policy
Economic-Statistical Design
non-central chi-square chart
adaptive control chart